Run
10459796

Run 10459796

Task 3711 (Supervised Classification) elevators Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectKBest,step_1=sklearn.linear_model._stochastic_gradient.SGDClass ifier)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_alpha3.463268655959164e-05
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_averagetrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_class_weightnull
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_early_stoppingfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_epsilon44.2706155036079
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_eta00.6463501100668442
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_fit_interceptfalse
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_l1_ratio0.782748785355376
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_learning_rate"adaptive"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_loss"log"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_max_iter201
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_iter_no_change36
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_n_jobs1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_penalty"l2"
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_power_t0.568762667026968
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_random_state42
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_shuffletrue
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_tol0.3794896012612857
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_validation_fraction0.1
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_verbose0
sklearn.linear_model._stochastic_gradient.SGDClassifier(2)_warm_startfalse
sklearn.feature_selection._univariate_selection.SelectKBest(1)_k17
sklearn.feature_selection._univariate_selection.SelectKBest(1)_score_func{"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._univariate_selection.f_classif"}
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)(1)_verbosefalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.5247 ± 0.0123
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6023 ± 0.0106
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0498 ± 0.0249
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0337 ± 0.0278
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.387 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4271 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.613 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
Number of instances
16599
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5945 ± 0.0109
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.613 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8921 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9062 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4621 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6221 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3461 ± 0.0195
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5237 ± 0.0118
Cross-validation details (10-fold Crossvalidation)